Wire Arc Additive Manufacturing Research Articles

Sensitivity study of process parameters of wire arc additive manufacturing using probabilistic deep learning and uncertainty quantification

This study employs a deep learning (DL) based stochastic approach to comprehensively interpret the effects of current intensity and velocity variations on temperature evolutions and cooling rates in the wire arc additive manufacturing (WAAM) process of a thin wall. Uncertainty raised from process parameters, material properties, and environmental conditions significantly impacts the final product quality. Furthermore, understanding the relationship between the process and temperature evolution within the WAAM process is complex. This study contributes to quantifying the uncertainty to the final product quality, such as temperature evolutions and cooling rates via a fast and accurate DL-based surrogate model. This contribution helps to precise adjustments and optimizations to enhance the overall WAAM process. Initially, a DL-based surrogate model is constructed using data obtained from a high-fidelity validated finite element (FE) model, ensuring an impressive 99% accuracy compared to the FE model while reducing computational costs. Subsequently, probabilistic methods are used to characterize uncertainties in current intensity and velocity, and the Monte-Carlo method is applied for uncertainty propagation. The findings illustrate that small variations in the input parameters can lead to significant fluctuations in temperature evolutions. Additionally, a sensitivity analysis is conducted to precisely quantify the influence of each input parameter. Finally, an uncertainty reduction is performed to enhance the variation of cooling rate. In general, this study is expected to make precise adjustments and optimizations to enhance the overall WAAM process for better quality of printed piece.

Effects of residual Laves phase on microstructural evolution and mechanical properties of wire arc additive manufactured GH4169 Ni-based superalloy

The GH4169 Ni-based superalloy is prepared by wire arc additive manufacturing and the sizes and morphologies of Laves phases under different heat treatment strategies are obtained to further investigate the influence of residual Laves phases on the tensile properties of the superalloy. After the solution treated at a higher 1150 °C and followed by aging, the Laves phase is completely dissolved, with limited δ phase with varying sizes distributed at the grain boundaries. While for lower 1020 °C solid solution heat treatment temperature, the Laves phase in the sample changes from chain-like to particle-like shapes by partially dissolving, and the needle-like δ phase exists around the particle-like Laves phase. Such residual "Laves + δ" impede high-temperature tensile crack propagation, significantly enhancing plasticity. This mechanism makes the lower temperature solid solution treated sample exhibit similar tensile stress but ∼2 times of elongation when compared to the sample treated by higher solid solution.

Research Status and Development Trend of Wire Arc Additive Manufacturing Technology for Aluminum Alloys

It is difficult for traditional aluminum alloy manufacturing technology to meet the requirements of large-scale and high-precision complex shape structural parts. Wire Arc additive manufacturing technology (WAAM) is an innovative production method that presents the unique advantages of high material utilization, a large degree of design freedom, fast prototyping speed, and low cast. As a result, WAAM is suitable for near-net forming of large-scale complex industrial production and has a wide range of applications in aerospace, automobile manufacturing, and marine engineering fields. In order to serve as a reference for the further development of WAAM technology, this paper provides an overview of the current developments in WAAM both from the digital control system and processing parameters in summary of the recent research progress. This work firstly summarized the principle of simulation layering and path planning and discussed the influence of relative technological parameters, such as current, wire feeding speed, welding speed, shielding gas, and so on. It can be seen that both the welding current and wire feeding speed are directly proportional to the heat input while the travel speed is inversely proportional to the heat input. This process regulation is an important means to improve the quality of deposited parts. This paper then summarized various methods including heat input, alloy composition, and heat treatment. The results showed that in the process of WAAM, it is necessary to control the appropriate heat input to achieve minimum heat accumulation and improve the performance of the deposited parts. To obtain higher mechanical properties (tensile strength has been increased by 28%–45%), aluminum matrix composites by WAAM have proved to be an effective method. The corresponding proper heat treatment can also increase the tensile strength of WAAM Al alloy by 104.3%. In addition, mechanical properties are always assessed to evaluate the quality of deposited parts. The mechanical properties including the tensile strength, yield strength, and hardness of the deposited parts under different processing conditions have been summarized to provide a reference for the quality evaluation of the deposition. Examples of industrial products fabricated by WAAM are also introduced. Finally, the application status of WAAM aluminum alloy is summarized and the corresponding future research direction is prospected.

Correction: Improving microstructure and mechanical properties of thin-wall part fabricated by wire arc additive manufacturing assisted with high-intensity ultrasound

Correction: Improving microstructure and mechanical properties of thin-wall part fabricated by wire arc additive manufacturing assisted with high-intensity ultrasound

253MA heat-resistant stainless steel by wire arc additive manufacturing: microstructure and mechanical properties

ABSTRACT The 253MA stainless steel fabricated via wire arc additive manufacturing displays a mixed austenite and ferrite microstructure. It features many dislocations, minimal inclusions, and carbides, observed through TEM. Notably, CeO2 spherical inclusions enhance its high–temperature and oxidation resistance. Microhardness test 2 shows average values of 187.9 HV0.2 and 183.9 HV0.2 in transverse and longitudinal directions, respectively. Mechanical properties at room temperature include a yield strength of 388 MPa, a tensile strength of 681 MPa, 18.89% elongation, and 34% area reduction post–fracture. Impact toughness measured 89.7J and 133.7J in transverse and longitudinal directions, respectively. At 600°C, the steel's yield strength and tensile strength drop to 208 MPa and 412 MPa, with elongation increasing to 32.06% and area reduction to 53%. The stress–strain curve at 600°C shows serrated flow, suggesting dynamic strain ageing. SEM analysis indicates ductile fracture with larger dimples at elevated temperatures.

Augmenting microstructure and tribological performance of wire arc additive manufactured PH13-8Mo stainless steel via TiC/TiB2 nano-particles incorporation

This study aimed to investigate the effect of TiC and TiB2 nano-inoculants addition on the microstructure and tribological behavior of PH13-8Mo stainless steel processed through wire arc additive manufacturing (WAAM). The incorporation of TiC/TiB2 inoculants demonstrated notable efficiency in mitigating the anisotropic wear and scratch response and enhancing wear resistance observed in the as-printed state. This improvement was ascribed to the refinement of the grain structure, disruption of the columnar structure, and increased content of the retained austenite. Notably, TiB2 inoculation exhibited superior grain refinement and achieved the highest hardness. However, the TiC-inoculated condition demonstrated the best wear resistance, attributed to its excellent combination of hardness and fracture resistance, and a higher contribution of strain-induced martensite transformation during wear testing. Additionally, the implementation of post-printing solutionizing and aging treatment was found to improve the scratch and wear resistance of the alloy, attributed to the formation of nano-sized β-NiAl precipitates. The main wear mechanism observed involved oxidation wear, adhesive wear, and three-body abrasive wear. The findings of this study highlight the significant potential of incorporating ceramic-based nano-particles for improving the wear resistance of WAAM PH13-8Mo components, particularly in demanding applications like injection molding dies where superior abrasion resistance is paramount.

Microstructural Evolution, Mechanical and Electrochemical Performance of Duplex Stainless Steel Fabricated by Wire Arc Additive Manufacturing with ER2209 Filler Wire

Microstructural Evolution, Mechanical and Electrochemical Performance of Duplex Stainless Steel Fabricated by Wire Arc Additive Manufacturing with ER2209 Filler Wire

Wire-arc additive manufacturing of Mg-Gd-Y-Zn-Zr alloy: Microstructure and mechanical properties

Mg-Gd-Y-Zn-Zr alloy is an essential high strength weight ratio material in the aerospace field. It has been fabricated gradually using wire-arc additive manufacturing (WAAM), a key integrated preparation technology for large-scale parts. In this investigation, a Mg-9.4Gd-3.3Y-1.75Zn-0.38Zr (wt.%) alloy was prepared by WAAM based in the cold metal transfer (CMT) process. Investigations were conducted into the microstructural evolution and mechanical properties during the solution and aging heat treatment. With an average grain size of 18.30 ± 9.89 μm, the as-deposited Mg-Gd-Y-Zn-Zr alloy is mainly composed of α-Mg matrix, (Mg,Zn)3(Gd,Y) eutectic phase, RE-rich particles, and rare-earth (RE) segregation zone. Following a 12-h heat treatment at 500 °C, the average grain size of the α-Mg matrix showed no appreciable changes. The α-Mg matrix also dissolved the (Mg, Zn)3(Gd, Y) phases, which led to the production of long period stacking ordered (LPSO) phases and the disappearance of the RE-segregation zone. The appearance of the nanoscale β′ phase following aging heat treatment. The deposited alloys had the following tensile properties: 196.5 ± 24 MPa for ultimate tensile strength (UTS), 157.2 ± 14 MPa for yield strength (YS), and 2.08 ± 0.8% for elongation (EL). The formation of LSPO phase can improve the ductility while the formation of nanoscale β′ phase can improve the strength of Mg-Gd-Y-Zn-Zr alloy. Thus, after heat treatment at 500 °C for 12 h, the alloy's EL rose to 9.27 ± 0.94%. After further aging heat treatment for 225 °C at 24h, the alloy's YS rose to 234±3 MPa and its EL was 4.56 ± 0.25%.

Semi-supervised learning for real-time anomaly detection in pulsed transfer wire arc additive manufacturing

In Wire-Arc Additive Manufacturing (WAAM), ensuring the quality and integrity of components is of key importance and performing anomaly detection during production is an efficient strategy for preventing defects and maintaining an acceptable quality with lower costs compared to the development of complex supervised learning techniques. This paper presents an approach based on semi-supervised learning for real-time anomaly detection in the production of Inconel 718 components via the Pulsed Transfer WAAM process. The workflow is based on the use of training data obtained by employing established process parameters, similar to what is done in Welding Procedure Qualification Records (WPQRs), to develop a semi-supervised anomaly detection application. The proposed approach involves depositing material using the already established process parameters and collecting the corresponding welding data in terms of welding current and voltage sensor signals. The collected data are then employed to train an unsupervised algorithm by using solely good deposition data to learn hidden complex patterns of normality and hence detect anomalies based on deviations from these patterns. With this aim, global and local features are extracted from the welding current and voltage sensor signals in both time and time-frequency domains via Wavelet Transform technique and a deep learning approach based on a residual convolutional autoencoder. To showcase the effectiveness of the proposed approach, a case study involving wire arc additive manufacturing of Inconel 718 components was conducted. The proposed algorithm was tested and achieved an F-score of 0.895, representing a 30 % improvement over state-of-the-art methods that rely on time domain features for anomaly detection. This research work contributes to the improvement of anomaly detection methodologies, offering substantial advantages over alternative techniques for quality control and reliability of additive manufacturing processes such as WAAM.

Exploration microstructural evolution and wear mechanisms of wire arc additive manufacturing NiTi/Nb bionic composite materials

NiTi/Nb layered heterogeneous structure (LHS) materials with different layer spacings were designed and fabricated using composite wire arc additive manufacturing. The NiTi/Nb LHS samples consisted of NiTi phase and β-Nb phase, and no other intermetallic compounds were formed. The NiTi layer in the NiTi/Nb LHS sample changes from a homogeneous eutectic microstructure to a gradient heterogeneous eutectic microstructure as the layer thickness increases. The Nb layer grains are much smaller than the NiTi layer grains, and no obvious texture is formed in the NiTi/Nb LHS samples. NiTi/Nb LHS samples still have a certain recoverable strain at room temperature, with a maximum recoverable strain of 4.36 % after 10 cycles of 10 % strain compression. The wear rate of the NiTi layer of the NiTi/Nb LHS samples was lower than that of the Nb layer. The main wear mechanisms on the Nb surface are abrasive wear, adhesive and oxidative wear while the main wear mechanism on the NiTi surface is adhesive wear. When the layer thickness ratio of NiTi layer to Nb layer is 2:1, the NiTi/Nb LHS samples form a gradient heterogeneous eutectic structure in the NiTi region, which in turn enhances the wear resistance of the LHS samples. In this study, cross-scale NiTi alloy and Nb heterogeneous components were constructed by wire arc additive manufacturing, which showed good wear resistance. The design provides a new solution for integrated additive manufacturing repair of heterogeneous metals in engineering applications.

Trailblazing multi-material structure: Niobium alloy to tungsten–copper composite using wire-arc additive manufacturing

Integrating tungsten and niobium alloys into a single component offers synergistic benefits for fusion reactor applications. Additive manufacturing, particularly for components with complex geometries, further amplifies these advantages. This study examines the fabricability of a multi-material structure composed of a W–Cu composite and an Nb–Zr alloy using wire-arc additive manufacturing. The resulting W–Cu/Nb–Zr structure was free from significant defects like porosity or cracks, though the formation of hard intermetallic compounds was observed. Numerical analysis indicated considerable residual stress accumulation at the interface. During tensile testing, fractures occurred in the W–Cu composite near the interface, with the specimens exhibiting an ultimate tensile strength of 218 ± 3 MPa and an elongation of 5.7 ± 1 %.

The Effect of Interpass Temperature on the Mechanical Properties and Microstructure of Components Made by the WAAM Method from Inconel 718 Alloy

The following study examines the impact of temperature on the deposition of components using Cold Metal Transfer–Wire Arc Additive Manufacturing technology. In the experiment, two overlay weld wall structures were created by applying an interpass temperature of 100 °C and without additional cooling. Subsequently, the microstructural and mechanical properties were observed. No changes in the microstructure due to the application of the interpass temperature were confirmed, and the microstructure of the manufactured components, in both cases, consisted of columnar dendrites. It was found that applying an interpass temperature reduced the average ultimate tensile strength by nearly 65 MPa and the average offset yield strength by 82 MPa. The influence of the cooling strategy on the resulting microstructure was not confirmed. Transmission electron microscopy analysis confirmed the presence of strengthening phases γ′/γ″ in both components; however, a larger amount of the strengthening phase γ″ was found in the component manufactured without the application of an interpass temperature.

Improving the Interpretability of Data-Driven Models for Additive Manufacturing Processes Using Clusterwise Regression

Wire Arc Additive Manufacturing (WAAM) represents a disruptive technology in the field of metal additive manufacturing. Understanding the relationship between input factors and layer geometry is crucial for studying the process comprehensively and developing various industrial applications such as slicing software and feedforward controllers. Statistical tools such as clustering and multivariate polynomial regression provide methods for exploring the influence of input factors on the final product. These tools facilitate application development by helping to establish interpretable models that engineers can use to grasp the underlying physical phenomena without resorting to complex physical models. In this study, an experimental campaign was conducted to print steel components using WAAM technology. Advanced statistical methods were employed for mathematical modeling of the process. The results obtained using linear regression, polynomial regression, and a neural network optimized using the Tree-structured Parzen Estimator (TPE) were compared. To enhance performance while maintaining the interpretability of regression models, clusterwise regression was introduced as an alternative modeling technique along with multivariate polynomial regression. The results showed that the proposed approach achieved results comparable to neural network modeling, with a Mean Absolute Error (MAE) of 0.25 mm for layer height and 0.68 mm for layer width compared to 0.23 mm and 0.69 mm with the neural network. Notably, this approach preserves the interpretability of the models; a further discussion on this topic is presented as well.

Innovative Prozessketten in der Produktion

Abstract The hybrid process chain, consisting of wire arc additive manufacturing and post-processing, is a promising alternative to many established process chains but brings new challenges to the product development process. In several research projects at the Institute for Machine Tools and Industrial Management (iwb) at the Technical University of Munich, existing competencies in hybrid manufacturing are further developed. These research projects aim to enable hybrid manufacturing for new applications and to facilitate stable processes.

Detektion von Bindefehlern beim DED-Arc

Abstract Based on artificial intelligence (AI) developed for monitoring arc welding, this article presents a deep neural network for monitoring lack of fusion defects in wire arc additive manufacturing of aluminium. The aim is to detect defects in built-up volumes on the basis of weld source data. These can be successfully processed by the algorithm presented and a trained AI. The achieved accuracy of the network is > 90 percent.

Enhancing corrosion resistance of AZ31B magnesium alloy substrate in 3.5 % NaCl solution with Al-Si coating prepared by cold metal transfer-based wire arc additive manufacturing

In this study, we explored an innovative approach to enhance the corrosion resistance of AZ31B magnesium alloy in 3.5 wt% NaCl solution by applying an Al-Si coating using cold metal transfer (CMT)-based wire arc additive manufacturing (WAAM) technology. The findings indicate that the Al-Si coating improved the alloy’s corrosion performance, with a more noble self-corrosion potential (- 0.97 VSCE) and lower self-corrosion current density (3.68 μA/cm2) compared to untreated AZ31B. Long-term immersion tests demonstrated that AZ31B suffered from severe localized corrosion, while Al-Si coating maintained a uniform corrosion profile. Analysis of the corrosion products showed that the Al-Si coating primarily comprised oxidized Mg and hydroxide Al, with minimal oxidized Al and Si, in contrast to the mainly metallic and hydroxide Mg on AZ31B. By reducing the electrochemical activity and enhancing the stability of corrosion products, the Al-Si coating significantly improved the corrosion resistance of AZ31B substrate. This study suggests a promising avenue for advancing the durability of magnesium alloys in corrosive environments.

Unveiling the Characteristics of ER70S-6 low Carbon Steel Alloy Produced by wire arc Additive Manufacturing at Different Travel Speeds

AbstractWire Arc Additive Manufacturing (WAAM) produces metal components with crucial properties dependent on process parameters. Understanding the effects of these parameters on microstructure and mechanical properties is vital for optimizing WAAM. This study investigated the impact of varying travel speeds (TS) on the microstructure and mechanical properties of low carbon steel ER70S-6 alloy produced by WAAM process. The hypothesis centred on the impact of different TS values on heat input (HI) and cooling rates, and the subsequent effects on the resulting microstructure and mechanical properties of the deposited material. ER70S-6 alloy was deposited at three different TS: 120, 150, and 180 mm/min. Microstructure and mechanical properties (microhardness, tensile strength, elongation) were evaluated for each TS condition. Distinct microstructures were observed in the deposited samples, influenced by cooling rates at different TS. Distinct microstructures emerged in different regions of the deposits due to varying cooling rates at different TS. Higher TS (180 mm/min) significantly reduced pores and cracks while enhancing yield strength (YS) and ultimate tensile strength (UTS) up to 25.2 ± 0.77% elongation and 502.3 ± 3.17 MPa UTS, respectively. However, UTS remained slightly lower (93%) than the catalogued value for ER70S-6 (540 MPa), indicating a mild softening effect. TS significantly influenced the microstructure and mechanical properties of WAAM-produced ER70S-6 alloy. This study provides key insights into optimizing WAAM parameters for low carbon steel, paving the way for improved component production for diverse industrial applications.

Comparative Process Parameter Optimization For Wire Arc Additive Manufacturing (WAAM) of E120C-GH4 Metal Cored and ER120S-G Solid Wire

Comparative Process Parameter Optimization For Wire Arc Additive Manufacturing (WAAM) of E120C-GH4 Metal Cored and ER120S-G Solid Wire

Wire arc additive manufacturing of ZL205A: Heat input effect on the forming quality, pore defects and mechanical properties

The magnitude of heat input (HI) plays a pivotal role in determining the deposition quality of wire arc additive manufacturing (WAAM) components. This article studies the effects of high and low HI on the forming quality, microstructure, pore defects, and mechanical properties of ZL205A parts, providing a theoretical foundation for choosing optimal HI parameters in the utilization of WAAM technology to fabricate ZL205A structural components. In this study, four thin-walled walls, SP1, SP2, SP3, and SP4, were manufactured with HI of 300.12, 200.08, 163.70, and 138.51 J/mm, respectively. The findings show that the SP4 has the least side forming error, and the maximum deposition efficiency, resulting in the best forming quality. From microstructural studies，SP4 has the lowest average grain size, 72μm. The quantity of θ phases (Al2Cu) on the grain boundary and the quantity and size of θ' phases in the middle and bottom grains both diminish with decreasing HI. As the HI decreases, the porosity of the four samples shows a trend of first decreasing and then increasing, with the highest being 1.41 % of the SP1. This change is explained from the four stages of bubble nucleation, growth, detachment, and escape. Reducing HI can significantly improve the ultimate tensile strength (UTS), and yield strength (YS) of the specimen. The SP4 has the highest UTS and YS, which are 232.99 MPa and 101.20 MPa, respectively.

Influence of Welding Processes on the Microstructure and Mechanical Properties of Duplex Stainless Steel Parts Fabricated by Wire Arc Additive Manufacturing

Influence of Welding Processes on the Microstructure and Mechanical Properties of Duplex Stainless Steel Parts Fabricated by Wire Arc Additive Manufacturing